Omid - Challenge 73

data-challenges
advanced-exercises
🔰 Result Question Info A B C Date Product
Published

March 24, 2026

Illustration for Omid - Challenge 73

Challenge Description

🔰 Result Question Info A B C Date Product

Solutions

library(tidyverse)
library(readxl)

path = "files/CH-073 Custom splitter 2.xlsx"
input = read_xlsx(path, range = "B2:B15")
test  = read_xlsx(path, range = "D2:F24")

date_pattern = "[0-9]{4}\\/[0-9]{1,2}\\/[0-9]{1,2}"
product_quant_pattern = "([A-Z]+[0-9]+)"

result = input %>%
  mutate(
    Date = str_extract(Info, date_pattern),
    Info2 = str_remove(Info, date_pattern),
    prod_quant = map(Info2, ~ unlist(str_extract_all(.x, product_quant_pattern)))
  ) %>%
  unnest(prod_quant) %>%
  select(Date, prod_quant) %>%
  extract(prod_quant, into = c("Product", "Quantity"), regex = "([A-Z]+)([0-9]+)") %>%
  mutate(Quantity = as.numeric(Quantity))

identical(result, test)
# [1] TRUE

  • Logic:

    • Builds the intermediate columns that drive the final result

    • Parses the text patterns directly instead of relying on manual cleanup

  • Strengths:

    • The R solution stays close to the workbook rule and keeps the transformation compact.

  • Areas for Improvement:

    • The code assumes the sheet structure and source ranges remain stable.

  • Gem:

    • The strongest part of the solution is choosing the right intermediate representation before shaping the final output.
import pandas as pd
import re

path = "CH-073 Custom Splitter 2.xlsx"
input = pd.read_excel(path, usecols= "B", skiprows=1, nrows = 13)
test  = pd.read_excel(path, usecols= "D:F", skiprows=1)

date_pattern = r"\d{4}/\d{1,2}/\d{1,2}"
product_quant_pattern = r"([A-Z]+\d+)"

input['Date'] = input['Info'].apply(lambda x: re.search(date_pattern, x).group())
input['Info2'] = input['Info'].apply(lambda x: re.sub(date_pattern, '', x))
input['prod_quant'] = input['Info2'].apply(lambda x: re.findall(product_quant_pattern, x))
input = input.explode('prod_quant')
input[['Product', 'Quantity']] = input['prod_quant'].str.extract(r"([A-Z]+)(\d+)")
input['Quantity'] = input['Quantity'].astype("int64")
result = input[['Date', 'Product', 'Quantity']].reset_index(drop=True)

print(result.equals(test)) # True

  • Logic:

    • Reads the workbook ranges needed for the challenge

    • Parses the text patterns directly instead of relying on manual cleanup

  • Strengths:

    • The Python version follows the same rule in a direct dataframe-oriented implementation.

  • Areas for Improvement:

    • The code assumes the workbook layout remains stable, so any sheet redesign would require small adjustments.

  • Gem:

    • The implementation stays close to the original workbook rule instead of adding unnecessary abstraction.

Difficulty Level

This task is moderate:

  • The core logic is clear, but the correct transformation pattern is not obvious from the raw input.

  • The challenge combines multiple reshaping, grouping, or parsing steps.